Abstract: The present disclosure relates to method and device for scheduling of computing force network resources and computing resources. There provides a method of resource scheduling in a computing force network, wherein the computing force network comprises at least one node among which at least one forward path exists, and the method comprises the following steps: for each of the at least one node, acquiring node performance indicator of the node based on resource indicators of the node and at least one influence node related to the node; for each of the at least one forward path, acquiring path performance indicator of the forward path based on the performance indicators of nodes constituting the forward path; and in response to a business requirement for the computing force network, selecting at least one optimal forward path from the at least one forward path based on the forward path performance indicators of the at least one forward path.

Abstract: The present disclosure relates to a blockchain-based federated learning device, method and system. An electronic device for the blockchain-based federated learning is proposed, including a processing circuit configured to acquire first federated learning related information from a federated learning node, cause verifying whether the federated learning node is able to participate in federated learning based on the first federated learning related information through blockchain, and notify the federated learning side of indication information indicating federated learning nodes that are able to participate in federated learning, so that the indicated federated learning nodes that can participate in federated learning can perform data processing based on federated learning.

Abstract: An electronic device (400) on an artificial intelligence (AI) platform side, the AI platform is capable of interacting with an application side to perform at least one AI intelligentialization process with the same application configuration, the electronic device (400) includes a processing circuitry (402) which is configured to: receive a configuration request for the at least one AI intelligentialization process from an application side, the configuration request including information indicating the application configuration; and when the application configuration is satisfied, assign the application configuration for usage by the at least one AI intelligentialization process during execution.

Abstract: A computing provider side device and a computing scheduling management side device in a wireless communication system are proposed. The computing scheduling management side device can collect computing related information about shareable computing from the computing provider side, and information about an application on a computing consumer side that needs to utilize the computing, and perform computing scheduling for the application based on the information. The computing provider side device can determine and provide its shareable computing for being scheduled for the applications on the provider side, and such shareable computing also can be released so as to be reused by the computing provider side device.

Abstract: Embodiments of the present disclosure provide an artificial intelligence-based data processing method and apparatus, and relate to the technical field of a computing power network. The method includes: in response to at least one task request for a target application, acquiring at least one task respectively corresponding to the at least one task request; determining a second cluster respectively corresponding to each task from at least one first cluster corresponding to the target application, based on task information respectively corresponding to each task; and for each task, allocating the task to the second cluster corresponding to the task so that the second cluster performs the task based on a calculation computing power resource.

Abstract: Embodiments of the present application provide a data processing method, a data processing apparatus, an electronic device and a storage medium, and relate to the technical field of mobile communication network. The method includes: acquiring first data from a network element interface, the first data being business-related data; acquiring first target data by processing the first data; and transmitting the first target data to a second functional entity, for the second functional entity to generate a control instruction based on the first target data and second target data and to send the control instruction to the network element related to the control instruction. In embodiments of the present application, the collection and processing tasks of the business data are undertaken by the first functional entity, and the network element is freed from the collection and processing of the business data contributing a larger proportion of data.

Abstract: The present disclosure relates to customer experience perception based on federated learning. An electronic device for a service provider in a wireless communication system, the service provider capable of implementing customer experience perception with at least one other service provider in the wireless communication system through federated learning, the electronic device comprises a processing circuit configured to: determine training data for training a global model related to the customer experience perception based on the federated learning, wherein the training data is related to the customer's experience for a specific service/product/business; perform local model training by using the determined training data to obtain intermediate information related to local model training; and transmit the intermediate information to a coordinator, so that the coordinator can train a customer experience perception model by aggregating the intermediate information from the provider and at least one other provider.

Abstract: The present disclosure relates to federated learning in a telecom system. For a service request from a service requester, an intelligent service provider serves as a master intelligent service provider to perform federated learning in coordination with at least one additional intelligent service provider as slave intelligent service provider, and then merge the service-related information obtained from the at least one slave intelligent service and the service-related information of the master intelligent service provider so as to provide intelligent service for the service requester, or provide merged service-related information to slave intelligent service provider.

Abstract: Embodiments of the present application provide a data processing method, a data processing apparatus, an electronic device and a storage medium, and relate to the technical field of mobile communication network. The method includes: acquiring first data from a network element interface, the first data being business-related data; acquiring first target data by processing the first data; and transmitting the first target data to a second functional entity, for the second functional entity to generate a control instruction based on the first target data and second target data and to send the control instruction to the network element related to the control instruction. In embodiments of the present application, the collection and processing tasks of the business data are undertaken by the first functional entity, and the network element is freed from the collection and processing of the business data contributing a larger proportion of data.

Abstract: The present disclosure relates to federated learning in a telecom system. For a service request from a service requester, an intelligent service provider serves as a master intelligent service provider to perform federated learning in coordination with at least one additional intelligent service provider as slave intelligent service provider, and then merge the service-related information obtained from the at least one slave intelligent service and the service-related information of the master intelligent service provider so as to provide intelligent service for the service requester, or provide merged service-related information to slave intelligent service provider.

Abstract: An electronic device (400) on an artificial intelligence (AI) platform side, the AI platform is capable of interacting with an application side to perform at least one AI intelligentialization process with the same application configuration, the electronic device (400) includes a processing circuitry (402) which is configured to: receive a configuration request for the at least one AI intelligentialization process from an application side, the configuration request including information indicating the application configuration; and when the application configuration is satisfied, assign the application configuration for usage by the at least one AI intelligentialization process during execution.

Abstract: A device may collect network performance data associated with a user equipment of a network. The network performance data may include information associated with a plurality of performance indicators of the network. The device may process information associated with a first portion of the plurality of performance indicators to determine a first performance category experience score, and information associated with a second portion of the plurality of performance indicators to determine a second performance category experience score. The device may process the first performance category experience score and the second performance category experience score to determine a network experience score. The device may determine whether the network experience score satisfies a threshold value. The device may perform one or more actions based on determining that the network experience score satisfies the threshold value.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A device may receive information that identifies a first set of parameter values associated with a first set of access points. The first set of access points may be associated with a set of known access point quality scores. The device may generate a model based on the set of known access point quality scores and the first set of parameter values. The device may receive information that identifies a second set of parameter values associated with a second set of access points. The device may determine a set of access point quality scores, for the second set of access points, based on the second set of parameter values and the model. The device may provide information to permit an action to be performed in association with the second set of access points.

Abstract: A device may collect network performance data associated with a user equipment of a network. The network performance data may include information associated with a plurality of performance indicators of the network. The device may process information associated with a first portion of the plurality of performance indicators to determine a first performance category experience score, and information associated with a second portion of the plurality of performance indicators to determine a second performance category experience score. The device may process the first performance category experience score and the second performance category experience score to determine a network experience score. The device may determine whether the network experience score satisfies a threshold value. The device may perform one or more actions based on determining that the network experience score satisfies the threshold value.

Abstract: A Self-Organizing Network (SON) collects data pertaining to a first number of cells of a wireless network. The SON splits the collected data into a second number of groups, and, for each of the second number of groups, repeatedly set a third number of clusters to a different number between a low limit and a high limit. The SON, for each of the settings, clusters the cells into the third number of clusters and trains a deep neural network to perform a regression analysis on the third number of clusters. For each of the second number of groups, the SON also determines an optimum number of clusters based on the regression analyses, re-clusters the cells into the optimum number of clusters; and tunes engineering parameters based on the re-clustering to optimize performance of the wireless network and quality of experience pertaining to the wireless network.

Abstract: A method, a device, and a non-transitory storage medium for estimating voice call quality include performing automatic speech recognition, for each of a plurality of voice calls, to generate recognized text for both an originating device acoustic signal and a receiving device acoustic signal. The recognized text for both the originating device acoustic signal and the receiving device acoustic signal are compared to the reference text to identified recognition errors and a voice call quality score for each of the originating device acoustic signal and the receiving device acoustic signal are determined. A correlation between the network conditions and the voice call quality scores is then determined.

Abstract: A device may receive cell property data associated with a cell in a mobile network and performance data associated with the cell. The device may determine whether the performance data associated with the cell satisfies a performance threshold. The device may identify, based on determining that the performance data associated with the cell satisfies the performance threshold, one or more impacted cells, in the mobile network, associated with the cell. The device may determine one or more antenna adjustment parameters based on at least the cell property data associated with the cell, the performance data associated with the cell, and performance data associated with the one or more impacted cells. The device may perform, based on the one or more antenna adjustment parameters, an action in connection with at least one of an antenna associated with the cell or another antenna associated with the one or more impacted cells.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.